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2019 | OriginalPaper | Chapter

Improving Robustness of Image Tampering Detection for Compression

Authors : Boubacar Diallo, Thierry Urruty, Pascal Bourdon, Christine Fernandez-Maloigne

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

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previous chapter Detecting Tampered Videos with Multimedia Forensics and Deep Learning
next chapter Audiovisual Annotation Procedure for Multi-view Field Recordings
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Metadata
Title
Improving Robustness of Image Tampering Detection for Compression
Authors
Boubacar Diallo
Thierry Urruty
Pascal Bourdon
Christine Fernandez-Maloigne
Copyright Year
2019
Book
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_32